Dieter,
You ask:
My question: can we trust this fit?
The answer depends on why you are doing the modelling.
If your goal is to describe the time course of concentrations then the
overall ability of the model to describe what you saw depends on the
totality of the model and its parameters. The model may be
overparameterized but it may still do what you want it to do i.e.
describe (and predict) the time course of concentrations in each
compartment. If you are satisfied with the VPC showing that simulations
from the model appropriately describe the observed concentrations then I
think the answer to your question is yes.
On the other hand if the goal is to estimate the size of one or more
critical parameters then you will need to pay attention to how well
these parameters are estimated. As Leonid has pointed out it seems that
at least some of the model parameters are not well identified. This may
be unimportant if the parameters you want to describe are robustly
estimated.
For example, if you had a simple PK model with samples mainly taken at
steady state with few observations during absorption then you may get a
good estimate of clearance but a rather poor estimate of KA. You
cannot simply remove a parameter such as KA (you have to describe the
sparse absorption somehow) but it will have little impact on the
clearance estimate. Thus the model can be trusted for the purpose of
estimating clearance but not absorption rate.
Nick
On 7/09/2010 12:11 a.m., Dieter Menne wrote:
Dear Nmusers,
we have very rich data from MRI concentration measurements, with 11
compartments and multiple compartments observed. The model is fit via SAEM
(nburn=2000), and followed by an IMPMAP as in the described in the 7.1.2
manual. OMEGA is band with pair-wise block correlations in the following
style:
$OMEGA BLOCK(2)
.02 ;CL
0.01 0.06 ; VC
$OMEGA BLOCK(2)
5.4 ; QMVP
0.001 0.05 ;VMVP
$OMEGA BLOCK(2)
0.06 ; QTVP
0.001 0.25 ;VTPV
$EST PRINT=1 METHOD=SAEM INTERACTION NBURN=2000 NITER=200 CTYPE=2 NSIG=2
FILE=SAEM.EXT
$EST METHOD=IMPMAP EONLY = 1 INTERACTION ISAMPLE=1000 NITER=5 FILE=IMP.EXT
$COV PRINT=E UNCONDITIONAL
Fits and CWRES diagnostics are perfect, and VPC checks are good.
However, we have negative eigenvalues (the following example has been edited
by removing digits)
ETAPval = 0.2 0.2 0.3 0.04 0.8 0.95 0.003 0.1 0.6 0.4 0.9 0.1 0.5 0.4 0.2
0.8 0.3 0.3 0.4 0.01 0.8
ETAshr% = 13. 0.4 38 20 23 33 46 30 18 41 54 22 2. 26. 49. 12. 0.07 24. 18.
35. 2.5
EPSshr% = 7.5 8.1
Number of Negative Eigenvalues in Matrix= 7
Most negative value= -65339.
Most positive value= 88796185.9
Forcing positive definiteness
Root mean square deviation of matrix from original= 1.37E-003
My question: can we trust this fit?
Dieter Menne
Menne Biomed/University Hospital of Zürich
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
